ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
| dc.contributor.author | Wang, Shengkun | en |
| dc.contributor.author | Bai, Yangxiao | en |
| dc.contributor.author | Fu, Kaiqun | en |
| dc.contributor.author | Wang, Linhan | en |
| dc.contributor.author | Lu, Chang-Tien | en |
| dc.contributor.author | Ji, Taoran | en |
| dc.date.accessioned | 2024-04-01T17:00:58Z | en |
| dc.date.available | 2024-04-01T17:00:58Z | en |
| dc.date.issued | 2023-11-06 | en |
| dc.date.updated | 2024-04-01T07:53:25Z | en |
| dc.description.abstract | For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3625007.3627488 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/118491 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |